The present invention relates to body part recognition in medical images, and more particularly, to deep learning based fine-grained body part recognition in medical images.
Deep learning techniques have received much attention in recent years. With computing power increasing due to modern graphics processing units (GPUs) and large labeled datasets, such as ImageNet and PASCAL VOC, deep learning architectures such as convolutional neural networks (CNNs) have been applied to many computer vision problems, such as image categorization, object detection, and image quality assessment. Recently, there have been many efforts to apply deep learning techniques to medical imaging tasks.
Although deep learning architectures, such as CNNs, have achieved impressive progress in many computer vision problems, the use of deep learning architectures becomes much more complicated in the medical imaging domain. For CNNs, a large labeled image set is typically required for adequate network training. However, collecting large-scale medical and annotations requires much expense, expertise, and time, which makes training a CNN from scratch unaffordable. One possible solution is to learn the network in an unsupervised manner. However, existing unsupervised learning methods do not perform well on learning meaningful representations for discrimination tasks. One way that has been proposed to alleviate the lack of annotated training samples is to pre-train a network on large-scale natural image datasets (e.g., ImageNet) and then fine-tune the network parameters for specific tasks. This kind of knowledge transfer is not only feasible, but in many cases is superior to training a CNN from scratch in terms of accuracy. Nevertheless, though most natural images and medical images share many low-level features, they still differ considerably in object-level structures. Thus, transfer learning from natural image data to medical applications may bring substantial bias which can possibly damage the experimental performance of the CNN.
CNN-based methods have been developed for body part recognition in medical imaging data. However, previous CNN-based body part recognition techniques remain at a coarse level, while real-world applications require more precise body part recognition.